Compression of noisy Bayer pattern color filter array images

Bayer Pattern Color Filter Arrays (CFAs) are widely used in digital photo and video cameras. Generally these images are corrupted by a signal and exposure dependent quantum noise. An automatic image processing carrying out within camera usually implies a gamma and color corrections and an interpolation. And at the same time the noise in the image becomes non-quantum and spatially correlated. This results in a drastic decrease of posterior noise reduction. Considerably better quality of output images can be provided if non-processed Bayer Pattern CFAs (in RAW format) are extracted from a camera and processed on PC. For this case, more effective noise reduction can be achieved as well as better quality image reconstruction algorithms can accessed. The only drawback of storing images in a camera in RAW format is their rather large size. Existing lossless image compression methods provide image compression ratios (CRs) for such images of only about 1.5...2 times. At the same time, a posterior filtering in addition to noise reduction results in appearing losses in the image. Therefore, the use of lossy image compression methods is defensible in this case while final decreasing of effectiveness of noise reduction is inessential. The paper describes a method of adaptive selection of quantization step for each block of a Bayer Pattern CFAs for DCT based image compression. This method allows restricting the decreasing of the posterior noise reduction by only 0.25...0.3 dB. Achieved CRs for the proposed scheme are by 2.5...5 times higher than for strictly lossless image compression methods.